Satellite Image Clustering

  • Surekha Borra
  • Rohit Thanki
  • Nilanjan Dey
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Remote Sensing technology senses and measures the radiation or reflectance of samples of distant objects, and allows extraction of information which includes detection and recognition of objects and its coverage. Image classification methods identify the objects represented by each pixel in the satellite image based on its spectral wavelength and time series. In this chapter, the basics of satellite image classification and its types are presented. The unsupervised classification methods such as K-means, Gaussian mixture model, self-organizing maps, and Hidden Markov models are described for clustering of satellite images.


Clustering K-means Gaussian mixture model Hidden Markov model Self-organizing maps Unsupervised 


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Copyright information

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Surekha Borra
    • 1
  • Rohit Thanki
    • 2
  • Nilanjan Dey
    • 3
  1. 1.Department of Electronics and Communication EngineeringK.S. Institute of TechnologyBengaluruIndia
  2. 2.Faculty of Technology and Engineering, Department of ECEC. U. Shah UniversityWadhwan cityIndia
  3. 3.Department of Information TechnologyTechno India College of TechnologyKolkataIndia

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